We developed the language of Modifiable Temporal Belief Networks (MTBNs) as a structural and temporal extension of Bayesian Belief Networks (BNs) to facilitate normative temporal and causal modeling under uncertainty. In this paper we present definitions of the model, its components, and its fundamental properties. We also discuss how to represent various types of temporal knowledge, with an em...

In this article we demonstrate the usefulness of causal Bayesian networks as probabilistic reasoning systems. The biggest advantage of causal Bayesian networks over traditional probabilistic Bayesian networks is that they sometimes allow to perform causal inference, i.e. the calculation of the causal effect of one variable on other variables. We treat a state-of-the-art algorithm for performing...

We investigate the applicability of artificial neural networks (ANNs) in reconstructing a sample image sponge-like microstructure. propose to reconstruct by predicting phase current pixel based on its causal neighbourhood, and subsequently, use non-causal ANN model smooth out reconstructed as form post-processing. also consider impacts different configurations (e.g., number densely connected la...

Investigations of American politics have increasingly turned to analyses of political networks to understand public opinion, voting behavior, the diffusion of policy ideas, bill sponsorship in the legislature, interest group coalitions and influence, party factions, institutional development, and other empirical phenomena. While the association between political networks and political behavior ...